With increasing development of science and technology, electronic devices in image applications become more popular. For example, image pickup devices such as web cameras, reversing vehicle cameras, door viewers or cameras are widely used to facilitate various needs of human lives. However, the image captured by the image pickup device is usually suffered from distortion to a certain extent. The distortion image is not pleasing to the user.
Generally, there are three main causes of the distortion image. Firstly, since the camera module comprises a multi-piece lens group, the light beam should be refracted many times along the optical path from the entrance to the image sensor. Under this circumstance, the distortion image is generated. Secondly, if the assembling quality of the image pickup device is deteriorated, the camera module and the image sensor are not in parallel with each other, and the multiple lenses of the camera module are not in parallel with each other. Since the center of the image sensor and the centers of the multiple lenses are not arranged along the same optical axis, the image is suffered from distortion. Thirdly, if the manufacturing error is high, especially for the low prince camera module, the image is readily suffered from distortion.
For solving the above drawbacks, various polynomial correction methods are used to correct the distortion image that is obtained by the image pickup device. In accordance with the concepts of the polynomial correction method, a basic geometric distortion pattern (e.g. a translation pattern, a scaling pattern, a rotation pattern, an affine pattern, a partial twisting pattern or a bending pattern) is mathematically modeled as a distortion polynomial. After the coefficients of the distortion polynomial are estimated, the distortion image can be geometrically corrected. However, if the distortion image is caused by two or more geometric distortion patterns (e.g. a combination of a partial twisting pattern and a scaling pattern), plural geometric distortion patterns should be mathematically modeled, and then the distortion image can be sequentially and geometrically corrected.
However, the use of mathematical modeling approach to compensate and correct the distortion image is only able to correct the ideal curvy distortion surface. In practice, since too many unexpected factors influence the assembling process of the image pickup device, the assembling process of the camera module and the fabricating process of the lens, the distortion image is not usually the ideal curvy distortion surface. In other words, the efficacy of correcting the distortion image is usually unsatisfied.
Recently, an image distortion correction method based on artificial intelligence has been disclosed. This image distortion correction method uses a neural network to describe the relationship between the distortion image and a standard image (i.e. an accurate non-distortion image) and train various parameters of the neural network. After the relationship between the distortion image and the standard image is established, the distortion image is corrected accordingly. In particular, for obtaining the pixel value of any point of the correction image (i.e. the image corrected from the distortion image), after the planar coordinate of this point is inputted into the neural network, the neural network may correspondingly output another planar coordinate. According to the outputted planar coordinate, the pixel value of this point may be obtained from the distortion image. The above technology of using the neural network to correct the distortion image is well known to those skilled in the art, and is not redundantly described herein.
Especially, the neural network is constructed according to the overall behaviors of various image distortion parameters. Consequently, it is not necessary to search the factors influencing the image distortion from the image pickup device and successively compensate the factors. Of course, it is not necessary to consider how to deal with the problem of resulting in the non-ideal curvy distortion surface. However, there are still some drawbacks. For example, since the neural network needs a very huge computing amount, a great number of neurons are required to construct the neural network. In other words, the fabricating cost is high.
Therefore, there is a need of providing an improved image distortion correction method in order to obviate the above drawbacks.